Factors Influencing Care Trajectories for Common Mental Disorders related sick leave : Patients’ Experience in Primary Care
Bibliographic record
Abstract
Context: Common mental health disorders (CMDs) represent up to 30% of all sick leave requests in 2013, with 25% lasting over 6 months. In Canada, family physicians serve as the main prescribers of sick leave and coordinate the necessary psychosocial and rehabilitation services for recovery and return to work (RTW). However, limited access to family physicians, their isolated practices, and time and knowledge constraints can impede effective patient sick leave management. Coupled with restricted service access, income disparities, and insurance coverage variations, this leads to trajectory variability, hindering optimal care establishment and creating patient inequity, ultimately undermining universal service quality. Objective: Describe patients’ perspectives of the factors having influenced their mental health-related sick leave trajectories and their access to primary care mental health and RTW services. Methods: A descriptive qualitative research design was used. Semi-structured individual interviews of approximately 60 minutes were conducted with 14 participants on a videoconference platform. Transcriptions were analyzed using Conventional content analysis. Setting: Participants were recruited mostly from the Montreal region in Quebec, Canada, were all followed by a family physician during their sick leave and had various access to insurance coverage. Results: Six themes describing the main factors influencing care trajectory were identified : 1) fragmented interventions provided by family physicians; 2) patients’ autonomy in managing their own care trajectory; 3) The attitude and case management provided by the insurer; 4) duration and ill-adapted intervention approaches of the Employee and Family Assistance Programs; 5) employer’s openness and understanding; and 6) match between the person’s needs and their ability to access psychosocial and rehabilitation services. Conclusions: Our findings emphasize crucial gaps in collaborative practices surrounding management of mental health-related sick leave. Strengthening coordination of these services, including the integration of return-to-work coordinators in primary care, is essential. Occupational therapists in this role could support the family physician in managing the sick leave and RTW, strengthen interprofessional and intersectoral collaboration, and ensure that patients received needed services in a timelier manner regardless of their insurance coverage or financial needs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".